R Notebook

This notebook contains several tSNE plots of the 10x data.

library(Seurat)

Loading the data.

all10x <- readRDS('data/10x-normalized-scaled-clustered-tsne.rds')

Below are several tSNE plots of the 10x data. tSNE was performed on the first 15 principal components of the log-normalized scaled (nUMI and percent.mito regressed out) data.

TSNEPlot(all10x, pt.size=0.1, group.by='sample_name', do.label=T)

TSNEPlot(all10x, group.by='sample_name2', pt.size=0.1)

TSNEPlot(all10x, pt.size=0.1, group.by='res.0.1')

TSNEPlot(all10x, pt.size=0.1, group.by='res.0.2')

TSNEPlot(all10x, pt.size=0.1, group.by='res.0.5', do.label=T)

TSNEPlot(all10x, pt.size=0.1, group.by='res.0.8', do.label=T)

FeaturePlot(all10x, c("nGene"), cols.use = c("grey","blue"))

FeaturePlot(all10x, c("percent.mito"), cols.use = c("grey","blue"))

The cluster with the higher percentage of mitochondrial gene expression probably contains stressed cells. Among the top 10 marker genes for this cluster are two genes that code for heat shock proteins (HSPA5 and HSP90B1), so this confirms this I think.

FeaturePlot(all10x, c("nUMI"), cols.use = c("grey","blue"))

TSNEPlot(all10x, group.by='diff', pt.size=0.1)

TSNEPlot(all10x, group.by='ucp1.ctrl', pt.size=0.1)

TSNEPlot(all10x, group.by='ucp1.ne', pt.size=0.1)

TSNEPlot(all10x, group.by='bmi', pt.size=0.1)

TSNEPlot(all10x, group.by='age', pt.size=0.1)

#TODO:
#add grid tSNE plots, one cluster colored at a time (res.0.5 and res.0.8)

#c <- all10x@meta.data$res.0.5
#cluster_names <- unique(c)